Irrigation
M. Komasi; A. Alizadefard
Abstract
Introduction: The occurrence of successive droughts, along with increasing water needs and lack of proper management of water resources has caused a water crisis that has various environmental and economic consequences. In addition to the drought, the change in the cropping pattern towards water crops ...
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Introduction: The occurrence of successive droughts, along with increasing water needs and lack of proper management of water resources has caused a water crisis that has various environmental and economic consequences. In addition to the drought, the change in the cropping pattern towards water crops has also made the water crisis the first critical phenomenon in recent years in the community, which has a direct impact on the agricultural sector as the largest consumer of water. Therefore, optimizing the cropping pattern is one of the most important factors in managing water resources and coping with water shortages. In this study, to determine the optimal cropping pattern of major crops in Silakhor plain in the next three years using two approaches using Linear Programming and Meta-Heuristic Algorithms. Materials and Methods: In the first step, in order to determine the optimal cropping pattern with the aim of maximizing farmers' incomes in the next three years and the limited water and land available, the amount of rainfall recharge is used as a criterion to determine the water exploitation interval and determine the minimum and maximum exploitation each year. In order to forecast rainfall, SARIMA time series models and Genetic Programming were used considering the data of the last 10 years in both seasonal and monthly modes, and according to RMSE and D.C. criteria, a better model was selected. Then, for each crop year, 100 exploitation scenarios were determined according to the amount of groundwater recharge caused by rainfall and the amount of exploitation in previous years. In the second step, Linear Programming was used to determine the optimal cropping pattern with the aim of maximizing farmers' incomes and limitations of exploitable water in each scenario and arable land. The price of each product is projected according to the average long-term inflation of the country, i.e., 20%, and the profit from the cultivation of each product was calculated as a proportion of the price of the product in each year by examining the previous years. Finally, the performance of three types of Static, Dynamic, and Classified Dynamics Penalty Functions into two algorithms, Differential Evolution and PSO was investigated to achieve the results obtained from Linear Programming. Static penalty functions use a constant value during the optimization process, whereas in dynamic penalty functions, the fines are modified during the process and depend on the number of generations. In the classified dynamics penalty, groups of violations are also determined, and the penalty of each response is determined according to the amount of violation of the restrictions and the generation number. Results and Discussion: The results show that with increasing groundwater exploitation, farmers' incomes also increase; However, in the exploitation of more than 223.5, 222.2, and 225.1 million cubic meters for the cropping years 2020-2021, 2021-2022, and 2022-2023, respectively, the limitation of the total arable land has prevented the increase of the area under cultivation, and by increasing exploitation, farmers' incomes remain stable. Also, in order to cultivate four crops of wheat, barley, rice, and corn with the current area under cultivation in Silakhor plain, 142 million cubic meters of water is harvested annually from underground sources. By optimizing the cropping pattern for the four crops studied, with the current water exploitation, the income of farmers in the region will increase by 18%. In general, the PSO algorithm answers this problem much faster. The average number of iterations of the PSO algorithm to solve each scenario in this problem is 38% of the number of iterations of the Differential Evolution algorithm. Overall, in solving this problem, the PSO algorithm has performed better in 84% of the scenarios. In penalty functions, the best performance in both algorithms belongs to the classified dynamics, dynamic, and static penalty functions, respectively. By changing the penalty function from static to classified dynamics penalty function, the number of iterations of the Differential Evolution algorithm to achieve the Linear Programming solution is reduced by an average of 11%; In contrast, the PSO algorithm did not react significantly to the change in the penalty function, and its repetitions decreased by an average of only 3%. Conclusion: The results show that the cropping pattern of the region is not optimal, and with the increase of water exploitation, it will move towards the cultivation of water products. Also, by optimizing the cultivation pattern of the region, farmers' incomes can be increased. Examination of Differential Evolution and PSO algorithms with three types of penalty functions also show that using the classified dynamics penalty function in the PSO algorithm can have good results.
Mohammad Reza Goodarzi; Alireza Faraji; Mahdi Komasi
Abstract
Introduction: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature, However, uncertainty in downscaling methods have been given less attention. Today many studies have been done about the future impact of climate change on human life and water resources. ...
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Introduction: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature, However, uncertainty in downscaling methods have been given less attention. Today many studies have been done about the future impact of climate change on human life and water resources. Urban development, water conflicts, and Green House Gases increasing will intensify this event in future and will alter rivers flow. Basin catchment has faced to flow recession and also runoff decreasing in few last decades. At this field the climate change effects will intensify this conditions in future decades too. The first step of climate change impacts studies is the projection of future climate variables (e.g precipitation and temperature). GCMS models and their outputs are useful tools for this projection. The main problem is the mismatch of spatial scale between the scale of global climate models and the resolution needed for impacts assessments.
Materials and Methods: The Gharesou River Basin is located in the west of Iran. Its area is approximately equal to 5793km2, and the maximum and minimum of its heights are 1237 and 3350 m, respectively. The average of annual rainfall varies from 300 to 800mm. This study focuses on various climate models from IPCC fourth and fifth reports and has been used two downscaling methods including the statistical and proportional downscaling methods and also scenarios and different climate models for considering different uncertainty. The new scenarios as Representative Concentration Pathways (RCPs) of greenhouse gasses have been used in fifth assessment reports (AR5) of IPCC. The Representative Concentration Pathways describe four different 21st-century pathways of greenhouse gas (GHG) emissions and atmospheric concentrations, air pollutant emissions and land use. The RCPs represent the range of GHG emissions. Different kinds of downscaling method include 1) Proportional downscaling that is adding coarse-scale climate changes to higher resolution observations (the delta approach); 2) Statistical method (eg SDSM model; CLIGEN; GEM; LARS-WG and etc); 3) Dynamical method that is application of regional climate model using global climate model boundary conditions (e.g, RegCM3; MM5 and PRECIS). statistical downscaling method processes establish relating large scale climate features (e.g., 500 MB heights), predictors, to local climate (e.g, daily, monthly temperature at a point), predictands. The SDSM software reduces the task of statistically downscaling daily weather series into seven discrete processes that are consist of quality control and data transformation; screening of predictor variables; model calibration; weather generation (observed predictors); statistical analyses; graphing model output and scenario generation (climate model predictors). HEC-HMS (Hydrologic Modeling System) has been designed by HEC (Hydrologic Engineering Center) for simulation of precipitation-runoff processes in a drainage basin. The HEC-HMS simulation methods represent - Watershed precipitation and evaporation: These describe the spatial and temporal distribution of rainfall on and evaporation from a watershed. - Runoff volume: These address questions about the volume of precipitation that falls on the watershed: How much infiltrates on pervious surfaces? How much runoff of the impervious surfaces? When does it run off? - Direct runoff: including overland flow and interflow. These methods describe what happens as water that has not infiltrated or been stored on the watershed moves over or just beneath the watershed surface. Baseflow: simulate the slow subsurface drainage of water from a hydrologic system into the watershed’s channels.- Channel flow: These so-called routing methods simulate one-dimensional open channel flow, thus predicting time series of downstream flow, stage, or velocity, given upstream hydrographs. HEC-HMS includes several models for calculation of cumulative precipitation losses but only the SMA module is continuous (a module that simulates the losses for both wet and dry weather conditions). Other loss models are event based.
Results and Discussion: The results of criteria and models weighting show that CANESM2 and HADCM3 are better than other models for future temperature and precipitation projection for statistical downscaling and HADCM3 for future precipitation and HADGEM for future temperature assessment for Proportional downscaling. According to various scenarios, future temperature and precipitation projection (2040-2069 period for the statistical and 2040-2052 period for Proportional downscaling) have downscaled and have given to HEC-HMS model for future flow projection. Already the rainfall-runoff model has calibrated and validated base on observed flow data in reference period that daily coefficient of determine was 0.7 for calibrated period and 0.6 for validated period. Finally, flow variation has investigated that Most of GCMS represent increases in winter flows and reductions in other season flows.
